General

  • Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data (Drug Discovery Today 2021)

  • Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet (Drug Discovery Today 2020)

  • Data-Driven Strategies for Accelerated Materials Design (Accounts of Chemical Rrsearch 2021)

  • The Role of Machine Learning in the Understanding and Design of Materials (JACS 2020)

  • Rethinking drug design in the artificial intelligence era (Nature Reviews Drug Discovery 2020)

  • Chemists: AI Is Here; Unite To Get the Benefit (J Med Chem 2020)

  • Drug discovery with explainable artificial intelligence (Nature Machine Intelligence 2020)

  • Machine learning approaches to drug response prediction: challenges and recent progress (Precision Oncology 2020)

  • Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition (Methods 2020)

  • Towards reproducible computational drug discovery (J Cheminformatics 2020)

  • Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis (J Med Chem 2020)

  • Drug Research Meets Network Science: Where Are We? (J Med Chem 2020)

  • Chemist versus Machine: Traditional Knowledge versus Machine Learning Techniques (Trends in Chemistry30265-3) 2020)

  • Current Trends, Overlooked Issues, and Unmet Challenges in Virtual Screening (JCIM 2020)

  • What Will Computational Modeling Approaches Have to Say in the Era of Atomistic Cryo-EM Data? (JCIM 2020)

  • Deep Learning in Chemistry (JCIM 2019)

  • Deep learning in drug discovery: opportunities, challenges and future prospects (Drug Discovery Today 2019)

  • Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases (Briefings in Bioinformatics 2019)

  • Practical considerations for active machine learning in drug discovery (Drug Discovery Today 2019)

  • Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery (Chemical Review 2019)

  • Machine learning in chemoinformatics and drug discovery (Drug Discovery Today 2018)

  • The rise of deep learning in drug discovery (Drug Discovery Today 2018)

  • Deep learning for computational chemistry (J Comp Chem 2017)

Molecular Representation

  • Molecular representations in AI-driven drug discovery: a review and practical guide (J Cheminformatics 2020）

  • Cheminformatics in Natural Product-Based Drug Discovery (Molecular Informatics 2020)

  • Exploring chemical space using natural language processing methodologies for drug discovery (Drug Discovery Today 2020)

  • Molecular Representation: Going Long on Fingerprints (Chem30198-4) 2020)

  • Learning Molecular Representations for Medicinal Chemistry (J Med Chem 2020)

QSAR/Target Prediction

Model Interpretation/Uncertainty Estimation

  • Uncertainty quantification in drug design (Drug Discovery Today 2020)

  • Predicting With Confidence: Using Conformal Prediction in Drug Discovery (J Pham Sci30589-X/fulltext) 2020)

  • Interpretable Deep Learning in Drug Discovery (Preprint 2019)

  • Concepts and Applications of Conformal Prediction in Computational Drug Discovery (Preprint 2019)

Chemical Reaction

Molecular modeling and simulation

  • Deep integration of machine learning into computational chemistry and materials science (Preprint 2021)

  • Representations of molecules and materials for interpolation of quantum-mechanical simulations via machine learning (PrePrint 2020)

  • Machine learning approaches for analyzing and enhancing molecular dynamics simulations (Current Opinion in Structural Biology 2020)

  • Advances of machine learning in molecular modeling and simulation (Current Opinion in Chemical Engineering 2019)

Molecular Generation/Chemical Space

Automation

Beyond Chemistry

  • Deep Learning in Mining Biological Data (Cognitive Computation 2021)

  • Informatics for Chemistry, Biology, and Biomedical Sciences (JCIM 2021)

  • Image-based profiling for drug discovery: due for a machine-learning upgrade? (Nature Reviews Drug Discovery 2020)

  • A review of optical chemical structure recognition tools (J Cheminformatics 2020)

  • Deep Learning in Protein Structural Modeling and Design (Preprint 2020)

  • Deep learning: new computational modelling techniques for genomics (Nature Reviews Genetics 2019)

  • Proteochemometrics – recent developments in bioactivity and selectivity modeling (Drug Discovery Today 2019)

  • Opportunities and obstacles for deep learning in biology and medicine (J Royal Society Interface 2018)

  • Deep Learning in Biomedical Data Science (Annual Reviews 2018)

  • Deep learning for computational biology (Molecular Syntems Biology 2016)

  • Polypharmacology modelling using proteochemometrics (PCM): recent methodological developments, applications to target families, and future prospects (MedChemComm 2015)

  • Machine Learning on Graphs: A Model and Comprehensive Taxonomy

  • Generalizing from a Few Examples: A Survey on Few-Shot Learning

  • Meta-Learning in Neural Networks: A Survey

  • Explainable Deep Learning:A Field Guide for the Uninitiated

  • Pre-trained Models for Natural Language Processing: A Survey

  • Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence

  • A Gentle Introduction to Deep Learning for Graphs

  • A Survey on Multi-Task Learning

  • Automated Machine Learning: State-of-The-Art and Open Challenges

  • A Comprehensive Survey on Graph Neural Networks

  • On the Robustness of Interpretability Methods